A Python package that contains common data structures. Data structures within this package also contain associated search and sorting algorithms. The intention of this package is mostly a learning endeavor but, it may also be used for various build purposes. I hope you find it easy to understand and interact with. If you have any questions or comments, please feel free to reach out.
Further information regarding this package can be found on the Python Package Index: pydatastructs.
To get started, install the package:
pip install pydatastructsThen, import it into your project:
from pydatastructs import (
Stack,
Queue,
Tree,
BinarySearchTree,
LinkedList,
MaxHeap,
MinHeap
)A list or array based data structure with last-in-first-out (LIFO) properties.
- get()
- length()
- is_empty()
- push()
- pop()
- merge_sort()
from pydatastructs import Stack
my_stack = Stack(collection=[5, 3, 1, 4, 2])
length = my_stack.length()
if length < 6:
my_stack.push(0)
my_stack.merge_sort() # [0, 1, 2, 3, 4, 5]A list or array based data structure with first-in-first-out (FIFO) properties.
- get()
- length()
- is_empty()
- enqueue()
- dequeue()
- merge_sort()
from pydatastructs import Queue
my_queue = Queue(collection=[5, 3, 1, 4, 2])
queue_is_empty = my_queue.is_empty
if not queue_is_empty:
print(my_queue.dequeue())
my_queue.merge_sort() # [1, 3, 4, 5]A node based data structure where each node contains a value property and a children property. The children property is a collection of child nodes. Finally, each node itself is a tree or sub-tree.
- add()
- contains()
- depth_first_traversal()
- breadth_first_traversal()
from pydatastructs import Tree
my_tree = Tree(value=1)
my_tree.add(2)
my_tree.add(3)
my_tree.children[0].add(4)
my_tree.children[0].add(5)
my_tree.children[1].add(6)
my_tree.children[1].add(7)
result = []
def add_to_result(node: Tree):
result.append(node.value)
my_tree.depth_first_traversal(add_to_result)
print(result) # [1, 2, 4, 5, 3, 6, 7]
# my_tree:
# 1
# / \
# 2 3
# / \ / \
# 4 5 6 7A node based data structure where each node contains a value property and, a left and right property. The left and right properties point to potential child nodes. The left node's value will always be less than the parent node's value. The right node's value will always be greater than the parent node's value. Finally, each node itself is a tree or sub-tree.
- insert()
- contains()
- depth_first_traversal()
- breadth_first_traversal()
from pydatastructs import BinarySearchTree
my_binarysearchtree = BinarySearchTree(value=10)
values = [6, 14, 4, 12, 8, 16]
for val in values:
my_binarysearchtree.insert(val)
result = []
def add_to_result(node: BinarySearchTree):
result.append(node.value)
my_binarysearchtree.breadth_first_traversal(add_to_result)
print(result) # [10, 6, 14, 4, 8, 12, 16]
# my_binarysearchtree:
# 10
# / \
# 6 14
# / \ / \
# 4 8 12 16A node based data structure containing a head and tail property. The head points to the root node and, the tail points to the last node in the linked list. Each node has a value property and a next property, which points to the next node in the linked list.
- append()
- remove_head()
- find_node()
from pydatastructs import LinkedList
my_linkedlist = LinkedList(value=1)
values = [2, 3, 4, 5]
for val in values:
my_linkedlist.append(val)
node = my_linkedlist.find_node(3)
print(node.next.value) # 4A complete binary tree data structure represented as an array where, every parent node's value is greater than or equal to their child node's values.
- get()
- insert()
- remove_max()
from pydatastructs import MaxHeap
my_maxheap = MaxHeap()
values = [1, 2, 3, 4, 5, 6, 7]
for val in values:
my_maxheap.insert(val)
my_maxheap.get() # [7, 4, 6, 1, 3, 2, 5]
# my_maxheap:
# 7
# / \
# 4 6
# / \ / \
# 1 3 2 5A complete binary tree data structure represented as an array where, every parent node's value is less than or equal to their child node's values.
- get()
- insert()
- remove_min()
from pydatastructs import MinHeap
my_minheap = MinHeap()
values = [7, 6, 5, 4, 3, 2, 1]
for val in values:
my_minheap.insert(val)
my_minheap.get() # [1, 4, 2, 7, 5, 6, 3]
# my_minheap:
# 1
# / \
# 4 2
# / \ / \
# 7 5 6 3For support, feedback or, to report a bug, you may contact the maintainer:
Distributed under the MIT License.